In recent years, data-driven modeling approaches have gained considerable traction in various meteorological applications, particularly in the realm of weather forecasting. However, these approaches often encounter challenges when dealing with extreme weather conditions. In light of this, we propose GA-SmaAt-GNet, a novel generative adversarial architecture that makes use of two methodologies aimed at enhancing the performance of deep learning models for extreme precipitation nowcasting. Firstly, it uses a novel SmaAt-GNet built upon the successful SmaAt-UNet architecture as generator. This network incorporates precipitation masks (binarized precipitation maps) as an additional data source, leveraging valuable information for improved predictions. Additionally, GA-SmaAt-GNet utilizes an attention-augmented discriminator inspired by the well-established Pix2Pix architecture. Furthermore, we assess the performance of GA-SmaAt-GNet using real-life precipitation dataset from the Netherlands. Our experimental results reveal a notable improvement in both overall performance and for extreme precipitation events. Furthermore, we conduct uncertainty analysis on the proposed GA-SmaAt-GNet model as well as on the precipitation dataset, providing additional insights into the predictive capabilities of the model. Finally, we offer further insights into the predictions of our proposed model using Grad-CAM. This visual explanation technique generates activation heatmaps, illustrating areas of the input that are more activated for various parts of the network.

翻译：近年来，数据驱动建模方法在各类气象应用中（特别是在天气预报领域）取得了显著进展。然而，这些方法在处理极端天气条件时仍面临挑战。基于此，我们提出GA-SmaAt-GNet——一种新型生成对抗架构，通过两种策略提升深度学习模型在极端降水临近预报中的性能。首先，该架构基于成功应用的SmaAt-UNet网络构建了新型SmaAt-GNet生成器，该网络引入降水掩膜（二值化降水分布图）作为额外数据源，利用有效信息增强预测精度。其次，GA-SmaAt-GNet采用基于成熟Pix2Pix架构的注意力增强型判别器。我们使用荷兰真实降水数据集评估模型性能，实验结果表明，该模型在整体预测和极端降水事件预测方面均有显著提升。此外，我们对所提出的GA-SmaAt-GNet模型和降水数据集进行不确定性分析，进一步揭示模型的预测能力。最后，借助Grad-CAM可视化解释技术，通过生成激活热力图展示网络不同模块中高响应输入区域，为模型预测提供深层见解。